Purpose: Iran is among the high-risk leishmaniasis regions in the world. WHO recommends the use of GIS as an ideal tool for healthcare authorities to predict the evolution of a disease, delimit the risk of outbreaks and identify critical areas. The aim of this research is to find the association between the main species of Leishmania (L. major, L. tropica, L. infantum) dispersion and climatic variables in Iran.
Methods: All molecular-based reports of leishmaniasis from Iran between 1999 and 2021 were gathered from reliable medical sources. Meteorological data (air and soil temperatures, annual rainfall and humidity) of the country along the study period were obtained from the Iranian Climatological Research Centre. The data concerning species distribution and climatic conditions during this period were moved to a base-map through raster layers using ArcGIS 10.4.1 software. The relationship between parasitological and climatic models was examined using ANOVA.
Results: High risk area maps, based on the cut-off thresholds, were generated for Leishmania major, L. tropica and L. infantum. According to the molecular-based reports, the L. major distribution was significantly related to all climatic variables, while L. tropica was merely related to rainfall and humidity, and the L. infantum distribution was significantly associated with rainfall, soil and air temperatures.
Conclusion: The association between climatic conditions and Leishmania species distribution in Iran has been confirmed. Consequently, both, the relationship between climatic conditions and the geographical distribution of Leishmania species, and the use of GIS to better understand the spatial epidemiology of leishmaniasis, have been reaffirmed.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11001653 | PMC |
| http://dx.doi.org/10.1007/s11686-024-00811-4 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Enhancing Greenhouse Efficiency: Integrating IoT and Reinforcement Learning for Optimized Climate Control.
Sensors (Basel)
December 2024
Informática Industrial y Redes de Computadores (I2RC), University of Alicante, 03690 Alicante, Spain.
Automated systems, regulated by algorithmic protocols and predefined set-points for feedback control, require the oversight and fine tuning of skilled technicians. This necessity is particularly pronounced in automated greenhouses, where optimal environmental conditions depend on the specialized knowledge of dedicated technicians, emphasizing the need for expert involvement during installation and maintenance. To address these challenges, this study proposes the integration of data acquisition technologies using Internet of Things (IoT) protocols and optimization services via reinforcement learning (RL) methodologies.
View Article and Find Full Text PDFDeep FS: A Deep Learning Approach for Surface Solar Radiation.
Sensors (Basel)
December 2024
Department of Computer Engineering, Konya Food and Agriculture University, Konya 42080, Turkey.
Contemporary environmental challenges are increasingly significant. The primary cause is the drastic changes in climates. The prediction of solar radiation is a crucial aspect of solar energy applications and meteorological forecasting.
View Article and Find Full Text PDFCoffee Leaf Rust Disease Detection and Implementation of an Edge Device for Pruning Infected Leaves via Deep Learning Algorithms.
Sensors (Basel)
December 2024
Institute of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Japan.
Global warming and extreme climate conditions caused by unsuitable temperature and humidity lead to coffee leaf rust () diseases in coffee plantations. Coffee leaf rust is a severe problem that reduces productivity. Currently, pesticide spraying is considered the most effective solution for mitigating coffee leaf rust.
View Article and Find Full Text PDFA Shoot Phenological Study of Certain Bamboo Taxa Under Central European Climatic Conditions.
Plants (Basel)
December 2024
Institute of Landscape Architecture, Urban Planning and Garden Art, Hungarian University of Agriculture and Life Sciences (MATE), 2100 Gödöllő, Hungary.
This study aimed to understand the phenological changes in the shoots of temperate bamboo spp. grown in Hungary, with a focus on how these changes were influenced by local climatic conditions. Data collected over two years on shoot phenology were analyzed with weather variables, especially air temperature and soil temperature.
View Article and Find Full Text PDFEcophysiological and Molecular Analysis of Contrasting Genotypes for Leaf Senescence in Sunflower ( L.) Under Differential Doses of N in Soil.
Plants (Basel)
December 2024
Instituto de Biotecnología, UEDD INTA CONICET, Buenos Aires 1686, Argentina.
Leaf senescence in plants is the last stage of leaf development and is characterized by a decline in photosynthetic activity, an active degeneration of cellular structures, and the recycling of accumulated nutrients to areas of active growth, such as buds, young leaves, flowers, fruits, and seeds. This process holds economic significance as it can impact yield, influencing the plant's ability to maintain an active photosynthetic system during prolonged periods, especially during the grain filling stage, which affects plant weight and oil content. It can be associated with different stresses or environmental conditions, manifesting itself widely in the context of climate change and limiting yield, especially in crops of agronomic relevance.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!